Algorithmic Decision-Making in Difficult Scenarios

Christopher B. Rauch, Ursula Addison,Michael W. Floyd,Prateek Goel,Justin Karneeb,Ray Kulhanek,Othalia Larue, David H. Ménager, Mallika Mainali,Matthew Molineaux,Adam Pease, Anik Sen, J. T. Turner,Rosina Weber

AAAI Spring Symposia（2024）

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Abstract

We present an approach to algorithmic decision-making that emulates key facets of human decision-making, particularly in scenarios marked by expert disagreement and ambiguity. Our system employs a case-based reasoning framework, integrating learned experiences, contextual factors, probabilistic reasoning, domain-specific knowledge, and the personal traits of decision-makers. A primary aim of the system is to articulate algorithmic decision-making as a human-comprehensible reasoning process, complete with justifications for selected actions.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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【要点】：本文提出了一种模拟人类决策特点的算法决策方法，特别是在专家意见分歧和模糊情景下，通过案例推理框架提高决策的可解释性。

【方法】：研究采用案例推理框架，结合学习经验、情境因素、概率推理、特定领域知识以及决策者的个人特质。

【实验】：论文中未具体描述实验过程和数据集名称，但目标是使算法决策过程具有可解释性，并提供选择的行动依据。

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